Normal mode analysis as a method to derive protein dynamics information from the Protein Data Bank

Wako, Hiroshi; Endo, Shigeru

doi:10.1007/s12551-017-0330-2

Cited by 53 publications

(58 citation statements)

References 76 publications

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“…Such a result indicates that the long-range correlations are encoded in the native topology of the proteins. Secondly, we conduct normal mode analysis [31][32][33] for protein molecules, ideal polymer chains, and lattice systems. A similar scaling relation holds for polymers, lattices, and proteins, but the scaling coefficients are different.…”

Section: Introductionmentioning

confidence: 99%

Long-range correlation in protein dynamics: Confirmation by structural data and normal mode analysis

Tang

Kaneko

2020

PLoS Comput Biol

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Proteins in cellular environments are highly susceptible. Local perturbations to any residue can be sensed by other spatially distal residues in the protein molecule, showing long-range correlations in the native dynamics of proteins. The long-range correlations of proteins contribute to many biological processes such as allostery, catalysis, and transportation. Revealing the structural origin of such long-range correlations is of great significance in understanding the design principle of biologically functional proteins. In this work, based on a large set of globular proteins determined by X-ray crystallography, by conducting normal mode analysis with the elastic network models, we demonstrate that such long-range correlations are encoded in the native topology of the proteins. To understand how native topology defines the structure and the dynamics of the proteins, we conduct scaling analysis on the size dependence of the slowest vibration mode, average path length, and modularity. Our results quantitatively describe how native proteins balance between order and disorder, showing both dense packing and fractal topology. It is suggested that the balance between stability and flexibility acts as an evolutionary constraint for proteins at different sizes. Overall, our result not only gives a new perspective bridging the protein structure and its dynamics but also reveals a universal principle in the evolution of proteins at all different sizes.

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Section: Introductionmentioning

confidence: 99%

Long-range correlation in protein dynamics: Confirmation by structural data and normal mode analysis

Tang

Kaneko

2020

PLoS Comput Biol

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“…Since 2003, protein data bank Japan (PDBj.org), a worldwide protein data bank (ww-PDB) partner, provides ProMode elastic modal analyses of a large and growing number of PDB entries [26,27,39]. Initially configured to minimize coordinates to a nearby minimum of the potential energy surface, the current implementation of ProMode employs a GNMbased PDB-NMA [30] with the NCI spring constants between atoms i and j scaled according…”

Section: Promodementioning

confidence: 99%

Slow Normal Modes of Proteins are Accurately Reproduced across Different Platforms

ben‐Avraham

Tirion

2018

Preprint

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The Protein Data Bank (PDB) [1] contains the atomic structures of over 10 5 biomolecules with better than 2.8Å resolution. The listing of the identities and coordinates of the atoms comprising each macromolecule permits an analysis of the slow-time vibrational response of these large systems to minor perturbations. 3D video animations of individual modes of oscillation demonstrate how regions interdigitate to create cohesive collective motions, providing a comprehensive framework for and familiarity with the overall 3D architecture. Furthermore, the isolation and representation of the softest, slowest deformation coordinates provide opportunities for the development of mechanical models of enzyme function. The eigenvector decomposition, therefore, must be accurate, reliable as well as rapid to be generally reported upon. We obtain the eigenmodes of a 1.2Å 34kDa PDB entry using either exclusively heavy atoms or partly or fully reduced atomic sets; Cartesian or internal coordinates; interatomic force fields derived either from a full Cartesian potential, a reduced atomic potential or a Gaussian distance-dependent potential; and independently developed software. These varied technologies are similar in that each maintains proper stereochemistry either by use of dihedral degrees of freedom which freezes bond lengths and bond angles, or by use of a full atomic potential that includes realistic bond length and angle restraints. We find that the shapes of the slowest eigenvectors are nearly identical, not merely similar. *

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“…To resolve this issue, we adopt NMA using the elastic network model (ENM-NMA), where the potential functions are solely harmonic terms with equilibrium positions residing on the studied structure. [24][25][26][27][28][29][30][31][32][33][34][35][36] This method does not require energy optimization and is appropriate to describe only low frequency normal modes that express collective motion such as interdomain motion. In this study, we comprehensively compare the X-ray crystal structures of ECD taken from The Protein Data Bank (PDB), 37) and discuss the process of ECD conversion by adiabatic mapping between the tethered and the extended forms.…”

Section: Introductionmentioning

confidence: 99%

Geometrical Conversion of the EGFR Extracellular Domain by Adiabatic Mapping Combining Normal Mode Analysis of the Elastic Network Model and Energy Optimization

Nojima

Kiyota

Terashi

et al. 2019

CHEMICAL & PHARMACEUTICAL BULLETIN

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The activation of epidermal growth factor receptor (EGFR) involves the geometrical conversion of the extracellular domain (ECD) from the tethered to the extended forms with the dynamic rearrangement of the relative positions of four subdomains (SDs); however, this conversion process has not yet been thoroughly understood. We compare the two different forms of the X-ray crystal structures of ECD and simulate the ECD conversion process using adiabatic mapping that combines normal mode analysis of the elastic network model (ENM-NMA) and energy optimization. A comparison of the crystal structures reveals the rigidity of the intradomain geometry of the SD-I and-III backbone regardless of the form. The forward mapping from the tethered to the extended forms retains the intradomain geometry of the SD-I and-III backbone and reveals the trends to rearrange the relative positions of SD-I and-III and to dissociate the C-terminal tail of SD-IV from the hairpin loop in SD-II. The reverse mapping from the extended to the tethered forms complements the promotion of ECD conversion in the presence of epidermal growth factor (EGF).

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Normal mode analysis as a method to derive protein dynamics information from the Protein Data Bank

Cited by 53 publications

References 76 publications

Long-range correlation in protein dynamics: Confirmation by structural data and normal mode analysis

Long-range correlation in protein dynamics: Confirmation by structural data and normal mode analysis

Slow Normal Modes of Proteins are Accurately Reproduced across Different Platforms

Geometrical Conversion of the EGFR Extracellular Domain by Adiabatic Mapping Combining Normal Mode Analysis of the Elastic Network Model and Energy Optimization

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